Google llc (20240112808). Interface for Patient-Provider Conversation and Auto-generation of Note or Summary simplified abstract

Interface for Patient-Provider Conversation and Auto-generation of Note or Summary

Organization Name

google llc

Inventor(s)

Melissa Strader of San Jose CA (US)

William Ito of Mountain View CA (US)

Christopher Co of Saratoga CA (US)

Katherine Chou of Palo Alto CA (US)

Alvin Rajkomar of San Jose CA (US)

Rebecca Rolfe of Menlo Park CA (US)

Interface for Patient-Provider Conversation and Auto-generation of Note or Summary - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112808 titled 'Interface for Patient-Provider Conversation and Auto-generation of Note or Summary

Simplified Explanation

The abstract describes a computer-implemented method that uses a neural network for text-to-image generation to generate an output image rendition of a scene based on a textual description.

• The method involves receiving a textual description of a scene and applying a neural network for text-to-image generation to create an image rendition of the scene.
• The neural network is trained to make image renditions associated with the same textual description attract each other and those associated with different textual descriptions repel each other.
• The method also includes predicting the output image rendition of the scene.

Potential Applications

This technology could be applied in various fields such as:

• Virtual reality and augmented reality applications
• E-commerce for generating product images based on descriptions
• Gaming industry for creating realistic environments

Problems Solved

This technology addresses the following issues:

• Generating images from textual descriptions accurately and efficiently
• Improving the quality of image renditions based on text inputs
• Enhancing the user experience in applications requiring text-to-image generation

Benefits

The benefits of this technology include:

• Streamlining the process of creating visual content from text
• Increasing productivity by automating image generation tasks
• Enhancing creativity and customization in image creation processes

Potential Commercial Applications

The potential commercial applications of this technology include:

• Image editing software with text-based image generation features
• Online platforms for personalized image creation services
• Content creation tools for digital marketing agencies

Possible Prior Art

One possible prior art for this technology could be the use of generative adversarial networks (GANs) for text-to-image generation.GANs have been widely used in the field of computer vision for generating realistic images from textual descriptions.

How does this technology compare to existing text-to-image generation methods in terms of accuracy and efficiency?

This article does not provide a direct comparison with other text-to-image generation methods, so it is unclear how this technology performs in relation to existing solutions.

What are the potential limitations or challenges of implementing this technology in real-world applications?

The article does not address any potential limitations or challenges that may arise when implementing this technology in practical scenarios.

Original Abstract Submitted

a computer-implemented method includes receiving, by a computing device, a particular textual description of a scene. the method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. the method further includes predicting the output image rendition of the scene.